3/18/2019

Introduction

Entrepreneurship and Development

  • Occupational change into self-employment
    • \(\uparrow\) economic development potential of entrepreneurship (Schumpeter, 1911; Kuznets, 1966)
    • \(\uparrow\) innovation (Schumpeter, 1934)
    • \(\downarrow\) poverty (Banerjee and Newman 1993)

Private Sector Development

Barriers to Entrepreneurship

  • Insufficient access to capital hinders entrepreneurship (Evans and Jovanovic 1989; Holtz-Eakin, Joulfaian, and Rosen 1994)

  • Credit constraints \(\uparrow\) ethnic minorities (Blanchflower et al., 2003; Fairlie and Robb,2008)
    • lower access to finance (Bond and Townsend 1996)
    • selection and discrimination (Robb 2013)

Financing Entrepreneurship

  • Rise of Microfinance… but criticisms exist
    • Loans don't reach the poor (Karlan and Zinman, 2010)
  • Credit constraints \(\uparrow\) for poor (Hurst and Lusardi 2004)

  • Conditional Cash Transfers (CCTs) Programs
    • Target poor individuals
    • Income eligibility, plus sometimes other conditions

Effect of CCTs on Entrepreneurship

  • Direct Effect :
    • \(\uparrow\) Entrepreneurship: Loosen financial constraints via income shock (Bianchi and Bobba 2013).
    • \(\downarrow\) Entrepreneurship: Over Dependent on cash transfers (Kanbur, Keen, and Tuomala 1994).
  • Indirect (Spillover) Effect:
    • Low financial market development \(\rightarrow\) reliance on informal financing (Tsai 2004)
    • Cash transfers: recipient \(\rightarrow\) non-recipient through interpersonal lending (Angelucci and Giorgi 2009)

Main Research Questions/Hypotheses

  1. What are effects of CCTs on transition into self-employment?

    H1a: Direct effect (+) via reduction in credit constraint

    H1b: Indirect effect (+) via interpersonal lending

  2. Is the size of the CCTs effects larger for ethnic minorities?

    H2a: Larger direct effect \(\rightarrow\) credit constraints \(\uparrow\)

    H2b: Larger indirect effect \(\rightarrow\) reliance on informal credit \(\uparrow\)

Policy Intervention: Dibao Program

  • China's Largest anti-poverty program: 50M recipients (8% rural pop.)
  • Implemented nationwide in 2007
  • Minimum income threshold eligibility, plus observables (sick/disabled)
  • Income eligibility rules; but no employment or repayment rules
Recipients (M) Dibao transfers ($) Average $/recipient
2007 35.7 2,675M 75
2008 43.1 4,220M 98
2009 47.6 6,283M 132
2010 52.1 7,456M 143
2011 53.1 10,877M 205
2012 53.5 11,599M 217
Growth\(_{07-12}\) 49.9% 334% 189%

Source: Ministry of Civil Affairs (various years).

Background: China's Ethnic Minorities

Ethnic Minorities in China

  • 104 million ethnic minorities
  • Classified into 55 different ethnic minority groups
  • State propaganda promotes ethnic unity (民族团结活动)

Ethnic Violence and State Response

104M Ethnic Minorities (Lagging Areas)

Source: China Census, 2010.

Source: China Statistical Yearbook, 2015.

Prior Fieldwork Experience

  • Ethnic-based disparities in income, mobility, credit access (A. Howell and Fan 2011; A. Howell 2011; Anthony Howell 2013)

Minimum Wage Policy and Ethnicity

  • Minimum Wages:
    • \(\downarrow\) location- and ethnic-based wage disparities
  • Counterfactual Analysis:
    • Average 26% increase in minimum wages from 2008 - 2010
    • \(\downarrow\) Gini Coefficient of urban wages by 10-12%

Source: Howell, 2019 (Journal of Urban Economics, Accepted)

Ethnic Entrepreneurship

  • Entrepreneurial households \(\uparrow\) income… but uneven gains

  • Minorities \(\downarrow\) likelihood of being self-employed; \(\downarrow\) reliance on formal finance

Source: Howell, 2019 (Small Business Economics)

China Context: Research Relevance

  • 50% of China's poverty counties in minority areas (WB, 2009)
    • Concerns about human rights and ethnic-wellbeing
  • Transition to self-employment…
    • Best chance to exit poverty (Banerjee and Newman 1993)
    • \(\downarrow\) chance participate in civil conflict (Miguel et al., 2004)
  • Due to lack of data availability in China…
    • Scant micro-evidence on ethnic minority employment
    • Lack of policy impact evaluation

Empirical Framework

Model Setup

  • Entrepreneurial Household:
    • 1+ HH member is self-employed or small business owner
    • Rural (subsistence) entrepreneurship: modest changes in HH activities \(\rightarrow\) poverty \(\downarrow\) (Bandiera et al. 2013)
  • Occupational choice framework (Evans and Jovanovic 1989)
  • Start business: \(E^a - K \geq E^h\)
    • expected business earnings (\(E^a\)) - investment costs (\(K\))
    • expected farm earnings (\(E^h\))

OC Model with Credit Constraints

The utility maximization problem is

\[\max_{m=\{0,1\}} \ u = Y + (1-m)E^h + m(E^a - k)\]

  • Due to credit constraints, poor families with income \(Y < K\) unable to finance new business
  • Targeted cash transfers (T) relax the credit constraints through an income effect
  • Previously credit constrained HH with income \(Y \in [K - T; K]\) able to finance the new business.

Data

  • 2012 China Household Ethnic Survey (CHES)

  • 1st statistically representative datasource

  • 300 villages; 50 counties; 7 provinces/regions
  • 14,576 urban/rural households

Rural CHES Sample Descriptives

Model Specification

Occupational choice modeled as binary choice Probit model:

\[M_{iv} = I(\beta_1 Dibao_v + \beta_2 {\bf{X}}_{iv} + \epsilon_{iv} \geq 0)\]

  • \(M_{iv}\): Self-employed status (1=yes, 0=no) of HH \(i\) in village \(v\)

  • \(Dibao_v\): share of Dibao beneficiary HH in village \(v\)

  • \(X_{iv}\): HH and village controls, plus region FE

  • \(\epsilon_{iv}\): error term

Identification Strategy

  • Identification Issue
    • Village targeting driven by unobservables, i.e. corruption
  • Quasi-Experimental Design:
    • Control Function Approach (Imbens & Wooldridge, 2007)
    • First-stage regression with exogenous variable, Z

\[Dibao_v = \alpha Z_v + \beta_2 {\bf{X}}_{v} + e\] where Z = 2007 share of disabled/sick HH in village \(v\)

  • Include residuals in occupational choice model

\[M_{iv} = I(\beta_1 Dibao_v + \beta_2 {\bf{X}}_{iv} + \gamma e + \epsilon_{iv} \geq 0)\]

Direct and Indirect Program effects

Estimate OC Model for eligible (E=1) and ineligible (E=0) HH,

\[M_{iv}^{E} = I(\beta_1 Dibao_v + \beta_2 {\bf{X}}_{iv} + \epsilon_{iv} \geq 0)\]

  • Eligibility defined by exogenous minimum income threshold
    • Baseline threshold \(\leq\) $180 (MCA, 2012)
    • Alternative thresholds: Poverty line ($385); 40% minimum wage
    • Avoids selection bias due to within village targeting

Main Results

  • Results Confirm Hypotheses

    H1: Positive (in)-direct effect of Dibao on self-employment

    H2: Size of (in)-direct effects \(\uparrow\) for ethnic minorities

Interpretation of Results

  • 1% expansion in Dibao coverage (+72 HH) increases self-employment probability by…

    • 0.55-0.85 p.p. for eligible HH (\(\uparrow\) 9-14 self-emp. HH )

    • 0.11-0.19 p.p. for ineligible HH (\(\uparrow\) 6-10 self-emp. HH)

  • Costs of 1% Program Expansion: $7,596
    • = 72 HH \(\times\) $105.5/HH
  • Private Benefits (Profits): $4,512-$7,356
    • = $164 profits/eligible HH \(\times\) 9 [14] eligible HH + $506 profits/Ineligible HH \(\times\) 6 [10] Ineligible HH

Robustness and Model Extensions

  • Results are robust
    • Confounding Factors: Exclude Wubao Beneficiaries
    • Isolate Credit Constraints: Exclude Insurance Holders
    • Sorting: Exclude Recent emigrants
  • Potential Mechanism: Informal, Inter-Household Lending
    • In higher Dibao coverage areas, ineligible HH more likely to engage in informal lending & borrowing
  • Potential Lending Channel: Ethnic-Based Networks
    • In areas w/ higher Han Dibao coverage, Han HH more likely to migrate but not minority HH, & vice versa

Implication of Findings

  • Income shock via CCTs \(\uparrow\) self-employment likelihood
    • \(\downarrow\) credit constraint
  • Larger (in-) direct effects on ethnic minorities…
    • ethnic minorities face \(\uparrow\) credit constraints
    • CCTs \(\downarrow\) ethnic-based disparities in entrepreneurship
  • Indirect effects appear to be driven by informal lending
    • Injection of liquidity from CCT fosters informal market

Candidate Background

Research Portfolio (Last 4 years)

Linking Administrative Datasets

Impact of Tax Reform on Innovation

  • Impact of corporate tax reform on firm innovation
  • Exploit 2004 VAT pilot tax reform in NE China
  • DID Design: \(Y_{it} = \beta_0 + \beta_1VAT_i + \beta_2 FC_t + \beta_3(VAT_i \times FC_t) + \epsilon_{it}\)

Source: Howell, A. (2016), Research Policy.

Technological Related Spillovers

Economic Development Zones

  • 2009: 20% of firms in some kind of EDZ
  • Measure Productivity Spillovers: Matching w/ DID & Event Analysis

\[Y_{ic}^{s} = \beta_0 + \delta After_{ic}^{s} + \beta_1 EDZ_{ic}^{ml} + \delta_1(After_{ic}^{s} \times EDZ_{ic}^{ml} ) + u_{ic}\]

  • 15% increase in productivity, s=0
  • Source: Howell, A., 2019, Journal of Regional Science

City Industry Encouragement

  • 7,000+ City Industrial Development Reports, 2001-2013

  • Counterfactual Results: Space-Neutral Policy \(\uparrow\) average TFP by 15%

Source: Howell, A., 2019, (R&R) Journal of Regional Science)

Land Grabs and Municipal Corruption

  • 1.5 Million land transactions at parcel level
  • Corruption proxy: gap between actual and paid price
  • Link to urban sprawl patterns across cities

Teaching Experience and Fit

  • Methods
    • Analytic Methods: Planning & Management
    • Urban GIS
    • Statistical Techniques: Geog. & Reg. Dev.
    • Spatial Analysis and Modeling
  • Thematic
    • Human Geography & Global Systems
    • Urban Growth & Development
    • Problems in Regional Development
    • The Chinese City

Note on FC proxy

Dynamic Investment Model: generalized method of moments (GMM) estimator

\[\Bigg(\frac{I_{it}}{K_{it-1}}\Bigg) = \theta_{0} + \Bigg(\theta_{1} \frac{I_{it-1}}{K_{it-2}} \Bigg) +\Bigg( \theta_{2} \frac{Sales_{it-1}}{K_{it-1}}\Bigg) + \Bigg( \theta_{3} \frac{Cash_{it-1}}{K_{it-1}} \times \Omega\Bigg) + \alpha_i + \delta_t + \epsilon_{it}\]

with \[\Omega = \lambda_1 \mathrm{ln(Size)_{it}} + \lambda_2 \mathrm{ln(Age)_{it}} + \lambda_3 \mathrm{Levg_{it}} + \lambda_4 \mathrm{Export_{it}} + \lambda_5 \mathrm{Subsidies_{it}} + \delta_{1} \mathrm{Industry_{1}}+ ... + \delta_{N}\mathrm{ Industry_{N}}.\]

The firm level FC score is derived as follows:

\[\hat{F}_{it} = \hat{\lambda}_1 \mathrm{Size_{it}} + \hat{\lambda}_2 \mathrm{Age_{it}} + \hat{\lambda}_3 \mathrm{Levg_{it}} + \hat{ \lambda}_4 \mathrm{Export_{it}} + \hat{\lambda}_5 \mathrm{Subsidies_{it}} + \hat{\delta}_{n}\mathrm{Industry_{n}}\]

Note on DID Design

Set up Difference-in-Differences Design \[ Y_{it} = \beta_0 + \beta_1Treatment_{i} + \beta_2 Post_{t} + \beta_3(Treatment_i \times Post_{t}) + \epsilon_{it}\]

  • DID estimator (\(\beta_3\)):

    • Impacts of Treatment on Outcome Y
  • Estimated with fixed-effects regression model
  • Parallel trend assumption satisfied
  • Treatment exogenous to anticipations of subject (firm, households)

Note about IV

  • IV Assumptions
    • Strongly determines Dibao coverage variations across villages
    • Is unrelated to household occupational choice decision except through Dibao channel
  • Possible violation of IV validity…
    • If more disabled villages are have more/less established entrepreneurial networks
  • To close this potential channel:
    • Proxy for local entrepreneurship network: 2007 Share of self-employed households in village \(v\)

Note about Variable Controls

  • HOH/HH controls: physical capital, family structure, political connections and wealth profiles
    • ethnicity; age; education status; language skills; CPC membership; # of working-age adults; share of females; dependency rate; per capita arable land; # durable goods
  • Village controls: economic development, physical endowments, public investments, natural disasters, and political connections of the village
    • Share of households with poor sanitation; per capita arable land; distance to nearest transportation center; share of self-employed households in 2007; mountainous area; infrastructure investment; natural disaster; leader who was promoted to upper levels of the government

References (Partial List)

Angelucci, Manuela, and Giacomo De Giorgi. 2009. “Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles’ Consumption?” American Economic Review 99 (1): 486–508.

Bandiera, Oriana, Robin Burgess, Narayan Das, Selim Gulesci, Imran Rasul, and Munshi Sulaiman. 2013. “Can Basic Entrepreneurship Transform the Economic Lives of the Poor?” Discussion Papers 7386, Institute for the Study of Labor (IZA).

Banerjee, A.V, and A.F. Newman. 1993. “Occupational Choice and the Process of Development.” Journal of Political Economy 101: 274–98.

Bianchi, Milo, and Matteo Bobba. 2013. “Liquidity, Risk, and Occupational Choices.” Review of Economic Studies 80 (2): 491–511.

Bond, Philip, and Robert Townsend. 1996. “Formal and informal financing in a Chicago ethnic neighborhood.” Federal Reserve Bank of Chicago, no. July: 3–27.

Evans, David S., and Boyan Jovanovic. 1989. “An Estimated Model of Entrepreneurial Choice under Liquidity Constraints.” Journal of Political Economy 97 (4): 808–27.

Holtz-Eakin, Douglas, David Joulfaian, and Harvey S. Rosen. 1994. “Entrepreneurial Decisions and Liquidity Constraints.” RAND Journal of Economics 25 (2): 334–47.

Howell, A. 2011. “Labor Market Segmentation in Urumqi, Xinjiang: Exposing Labor Market Segments and Testing the Relationship between Migration and Segmentation.” Growth and Change 42 (2). doi:10.1111/j.1468-2257.2011.00550.x.

Howell, A., and C. Fan. 2011. “Migration and inequality in Xinjiang: A survey of Han and Uyghur migrants in Urumqi.” Eurasian Geography and Economics 52 (1). doi:10.2747/1539-7216.52.1.119.

Howell, Anthony. 2013. “Chinese Minority Income Disparity in Urumqi: An Analysis of Han-Uyghur Labour Market Outcomes in the Formal and Informal Economies.” China: An International Journal 11 (3): 1–23.

Hurst, E., and A. Lusardi. 2004. “Liquidity Constraints, Household Wealth and Entrepreneurship.” Journal of Political Economy 112: 319–47.

Kanbur, R., M. Keen, and M. Tuomala. 1994. “Labor Supply and Targeting in Poverty Alleviation Programs.” The World Bank Economic Review 8 (2): 191–211.

Robb, Alicia. 2013. “Access to capital among young firms, minority-owned firms, women-owned firms, and high-tech firms.” Washington, DC: Office of Advocacy, US Small Business Administration.

Tsai, Kellee S. 2004. “Imperfect Substitutes: The Local Political Economy of Informal Finance and Microfinance in Rural China and India.” World Development 32 (9): 1487–1507.